{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize']=(15,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins=[1,2,3,4,5]\n",
    "data1=[1,2,3,4]\n",
    "data2=[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontdict = {'family': 'Times New Roman', 'weight': 'normal', 'size': 18}\n",
    "ax1=plt.subplot(121)\n",
    "n1=ax1.hist(data1,bins=bins,align='left',rwidth=0.5,density=True)[0]\n",
    "ax1.set_ylim((0,0.5))\n",
    "ax1.set_xlabel('(a)',fontdict=fontdict)\n",
    "for i,j in zip(bins[:len(n1[n1>0])],n1[n1>0]):\n",
    "    ax1.text(i-0.15,j+0.01,'%s'%j,fontdict=fontdict)\n",
    "ax2=plt.subplot(122)\n",
    "n2=ax2.hist(data2,bins=bins,align='left',rwidth=0.5,density=True)[0]\n",
    "ax2.set_ylim((0,1.2))\n",
    "ax2.set_xlabel('(b)',fontdict=fontdict)\n",
    "for i,j in zip(bins[:len(n2[n2>0])],n2[n2>0]):\n",
    "    ax2.text(i-0.15,j+0.01,'%s'%j,fontdict=fontdict)\n",
    "ax1.tick_params(labelsize=15)\n",
    "ax2.tick_params(labelsize=15)\n",
    "labels = ax1.get_xticklabels() + ax1.get_yticklabels()+ax2.get_xticklabels() + ax2.get_yticklabels()\n",
    "[label.set_fontname('Times New Roman') for label in labels]\n",
    "ax1.xaxis.set_major_locator(plt.MultipleLocator(1.0))\n",
    "ax2.xaxis.set_major_locator(plt.MultipleLocator(1.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
